Joint User Grouping and Linear Virtual Beamforming: Complexity, Algorithms and Approximation Bounds

TL;DR

This paper addresses the complex problem of joint node selection and linear beamforming in large wireless networks, proposing novel algorithms with guaranteed approximation bounds and demonstrating their effectiveness through numerical experiments.

Contribution

It introduces new semi-definite relaxation algorithms for joint node selection and beamforming with proven approximation guarantees in large-scale wireless systems.

Findings

Algorithms achieve guaranteed approximation bounds

Numerical experiments confirm effectiveness

Applicable to multiple transmission scenarios

Abstract

In a wireless system with a large number of distributed nodes, the quality of communication can be greatly improved by pooling the nodes to perform joint transmission/reception. In this paper, we consider the problem of optimally selecting a subset of nodes from potentially a large number of candidates to form a virtual multi-antenna system, while at the same time designing their joint linear transmission strategies. We focus on two specific application scenarios: 1) multiple single antenna transmitters cooperatively transmit to a receiver; 2) a single transmitter transmits to a receiver with the help of a number of cooperative relays. We formulate the joint node selection and beamforming problems as cardinality constrained optimization problems with both discrete variables (used for selecting cooperative nodes) and continuous variables (used for designing beamformers). For each application scenario, we first characterize the computational complexity of the joint optimization problem, and then propose novel semi-definite relaxation (SDR) techniques to obtain approximate solutions. We show that the new SDR algorithms have a guaranteed approximation performance in terms of the gap to global optimality, regardless of channel realizations. The effectiveness of the proposed algorithms is demonstrated via numerical experiments.